This notebook contains a set of analyses for analyzing nakedmeeple’s boardgamegeek collection. The bulk of the analysis is focused on building a user-specific predictive model to predict the games that the specified user is likely to own. This enables us to ask questions like, based on the games the user currently owns, what games are a good fit for their collection? What upcoming games are they likely to purchase?
We can look at a basic description of the number of games that the user owns, has rated, has previously owned, etc.
What years has the user owned/rated games from? While we can’t see when a user added or removed a game from their collection, we can look at their collection by the years in which their games were published.
We can look at the most frequent types of categories, mechanics, designers, and artists that appear in a user’s collection.
We’ll examine predictive models trained on a user’s collection for games published through 2020. How many games has the user owned/rated/played in the training set (games prior to 2020)?
username | dataset | period | games_owned | games_rated |
nakedmeeple | training | published before 2020 | 559 | 295 |
nakedmeeple | validation | published 2020 | 24 | 6 |
nakedmeeple | test | published after 2020 | 11 | 0 |
The main outcome we will be modeling for the user is owned, which refers to whether the user currently owns or has a previously owned a game in their collection. Our goal is to train a predictive model to learn the probability that a user will add a game to their collection based on its observable features. This amounts to looking at historical data and looking to find patterns that exist between features of games and games present in the user’s collection.
One of the models we trained was a decision tree, which looks for decision rules that can be used to separate games the user owns from games they don’t. The resulting model produces a decision corresponding to yes or no statements: to explain why the model predicts the user to own game, we start at the top of the tree and follow the rules that were learned from the training data.
Note: the tree below has been further pruned to make it easier to visualize.
Decision trees are highly interpretible models that are easy to train and can identify important interactions and nonlinearities present in the data. Individual trees have the drawback of being less predictive than other common models, but it can be useful to look at them to gain some understanding of key predictors and relationships found in the training data.
We can examine coefficients from another model we trained, which is a logistic regression with elastic net regularization (which I will refer to as a penalized logistic regression). Positive values indicate that a feature increases a user’s probability of owning/rating a game, while negative values indicate a feature decreases the probability. To be precise, the coefficients indicate the effect of a particular feature on the log-odds of a user owning a game.
Why did the model identify these features? We can make density plots of the important features for predicting whether the user owned a game. Blue indicates the density for games owned by the user, while grey indicates the density for games not owned by the user.
Binary predictors can be difficult to see with this visualization, so we can also directly examine the percentage of games in a user’s collection with a predictor vs the percentage of all games with that predictor.
% of Games with Feature | ||||
username | Feature | User_Collection | All_Games | Ratio |
nakedmeeple | ZMan Games | 9.8% | 1.2% | 8.03 |
nakedmeeple | GMT Games | 8.4% | 1.1% | 7.39 |
nakedmeeple | Rio Grande Games | 9.1% | 1.7% | 5.21 |
nakedmeeple | Eaglegryphon Games | 3.6% | 0.7% | 5.18 |
nakedmeeple | Games With Solitaire Rules | 21.6% | 4.8% | 4.53 |
nakedmeeple | Economic | 27.2% | 6.4% | 4.23 |
nakedmeeple | Asmodee | 9.3% | 2.4% | 3.80 |
nakedmeeple | Fantasy Flight Games | 3.8% | 1.1% | 3.37 |
nakedmeeple | 3D | 3.6% | 1.4% | 2.55 |
nakedmeeple | Ravensburger | 5.7% | 2.4% | 2.40 |
nakedmeeple | Dice Rolling | 28.6% | 28.3% | 1.01 |
nakedmeeple | Scenario Mission Campaign Game | 2.0% | 2.1% | 0.95 |
nakedmeeple | Trading | 2.5% | 3.0% | 0.83 |
nakedmeeple | Fantasy | 6.3% | 12.1% | 0.52 |
nakedmeeple | Miniatures Game | 2.0% | 4.9% | 0.40 |
nakedmeeple | Video Game Theme | 0.0% | 1.6% | 0.00 |
Before predicting games in upcoming years, we can examine how well the model did and what games it liked in the training set. In this case, we used resampling techniques (cross validation) to ensure that the model had not seen a game before making its predictions.
Displaying the 100 games from the training set with the highest probability of ownership, highlighting in blue games the user has owned.
Rank | Published | ID | Name | Pr(Owned) | Owned |
1 | 2013 | 143693 | Glass Road | 0.999 | yes |
2 | 2015 | 175878 | 504 | 0.989 | no |
3 | 2011 | 70919 | Takenoko | 0.982 | yes |
4 | 2017 | 220308 | Gaia Project | 0.977 | no |
5 | 2019 | 270971 | Era: Medieval Age | 0.974 | no |
6 | 1999 | 552 | Bus | 0.970 | yes |
7 | 2008 | 35677 | Le Havre | 0.967 | yes |
8 | 2010 | 62219 | Dominant Species | 0.963 | yes |
9 | 2010 | 70512 | Luna | 0.960 | yes |
10 | 2010 | 62227 | Labyrinth: The War on Terror, 2001 – ? | 0.958 | yes |
11 | 2017 | 193728 | Pendragon: The Fall of Roman Britain | 0.956 | yes |
12 | 2007 | 31260 | Agricola | 0.953 | yes |
13 | 2016 | 200680 | Agricola (Revised Edition) | 0.949 | no |
14 | 2014 | 159675 | Fields of Arle | 0.946 | yes |
15 | 2019 | 274364 | Watergate | 0.946 | yes |
16 | 2014 | 164928 | Orléans | 0.945 | no |
17 | 2019 | 286096 | Tapestry | 0.944 | no |
18 | 2011 | 70149 | Ora et Labora | 0.941 | yes |
19 | 2009 | 39683 | At the Gates of Loyang | 0.932 | yes |
20 | 2010 | 73439 | Troyes | 0.929 | yes |
21 | 2017 | 232988 | The Castles of Burgundy: The Dice Game | 0.925 | no |
22 | 2002 | 4098 | Age of Steam | 0.924 | yes |
23 | 2016 | 163154 | Falling Sky: The Gallic Revolt Against Caesar | 0.923 | yes |
24 | 2012 | 120677 | Terra Mystica | 0.921 | yes |
25 | 2019 | 276025 | Maracaibo | 0.920 | yes |
26 | 2012 | 111341 | The Great Zimbabwe | 0.911 | yes |
27 | 2000 | 475 | Taj Mahal | 0.905 | yes |
28 | 2014 | 159508 | AquaSphere | 0.903 | yes |
29 | 2019 | 253635 | Ragusa | 0.901 | no |
30 | 2014 | 146886 | La Granja | 0.898 | yes |
31 | 2016 | 167791 | Terraforming Mars | 0.897 | yes |
32 | 2009 | 55670 | Macao | 0.897 | yes |
33 | 2007 | 28720 | Brass: Lancashire | 0.887 | yes |
34 | 2016 | 177736 | A Feast for Odin | 0.881 | yes |
35 | 1997 | 42 | Tigris & Euphrates | 0.877 | yes |
36 | 2017 | 221805 | Breaking Bad: The Board Game | 0.877 | no |
37 | 2011 | 108687 | Puerto Rico | 0.869 | yes |
38 | 2017 | 199904 | Pericles: The Peloponnesian Wars | 0.868 | yes |
39 | 2019 | 220558 | Ancient Civilizations of the Inner Sea | 0.860 | no |
40 | 2016 | 191977 | The Castles of Burgundy: The Card Game | 0.858 | yes |
41 | 2002 | 3076 | Puerto Rico | 0.846 | no |
42 | 2017 | 199383 | Calimala | 0.842 | no |
43 | 2019 | 256960 | Pax Pamir: Second Edition | 0.841 | yes |
44 | 2011 | 84876 | The Castles of Burgundy | 0.840 | yes |
45 | 2018 | 199792 | Everdell | 0.840 | no |
46 | 2018 | 233080 | Book of Dragons | 0.834 | no |
47 | 2004 | 9216 | Goa | 0.833 | yes |
48 | 2016 | 176083 | Hit Z Road | 0.831 | no |
49 | 2012 | 122515 | Keyflower | 0.828 | yes |
50 | 2007 | 25554 | Notre Dame | 0.823 | yes |
51 | 2007 | 31594 | In the Year of the Dragon | 0.819 | no |
52 | 2018 | 244711 | Newton | 0.818 | no |
53 | 2007 | 27173 | Vikings | 0.818 | no |
54 | 1999 | 875 | Roads & Boats | 0.816 | yes |
55 | 2014 | 145371 | Three Kingdoms Redux | 0.811 | yes |
56 | 2015 | 175914 | Food Chain Magnate | 0.808 | yes |
57 | 1999 | 88 | Torres | 0.806 | yes |
58 | 2016 | 159692 | Comanchería: The Rise and Fall of the Comanche Empire | 0.805 | yes |
59 | 2010 | 66505 | The Speicherstadt | 0.802 | yes |
60 | 2018 | 247763 | Underwater Cities | 0.791 | yes |
61 | 2010 | 25292 | Merchants & Marauders | 0.790 | yes |
62 | 2006 | 25613 | Through the Ages: A Story of Civilization | 0.785 | no |
63 | 2014 | 148228 | Splendor | 0.782 | yes |
64 | 2013 | 144041 | Patchistory | 0.780 | no |
65 | 2012 | 128780 | Pax Porfiriana | 0.772 | yes |
66 | 2017 | 226254 | The Ruhr: A Story of Coal Trade | 0.768 | no |
67 | 1999 | 204 | Stephenson's Rocket | 0.766 | yes |
68 | 2019 | 257066 | Sierra West | 0.766 | no |
69 | 2014 | 144189 | Fire in the Lake | 0.765 | yes |
70 | 2017 | 216132 | Clans of Caledonia | 0.762 | no |
71 | 2013 | 137408 | Amerigo | 0.757 | yes |
72 | 2019 | 256730 | Pipeline | 0.751 | yes |
73 | 2018 | 256916 | Concordia Venus | 0.739 | no |
74 | 2016 | 169786 | Scythe | 0.734 | no |
75 | 2016 | 165872 | Liberty or Death: The American Insurrection | 0.731 | yes |
76 | 1995 | 93 | El Grande | 0.727 | no |
77 | 2015 | 173346 | 7 Wonders Duel | 0.726 | no |
78 | 2012 | 91080 | Andean Abyss | 0.720 | yes |
79 | 2012 | 119391 | Il Vecchio | 0.717 | no |
80 | 2017 | 161533 | Lisboa | 0.716 | yes |
81 | 1998 | 3 | Samurai | 0.713 | yes |
82 | 2017 | 233078 | Twilight Imperium: Fourth Edition | 0.711 | no |
83 | 2011 | 102680 | Trajan | 0.710 | yes |
84 | 2011 | 91873 | Strasbourg | 0.710 | yes |
85 | 2013 | 124361 | Concordia | 0.707 | yes |
86 | 2007 | 27708 | 1960: The Making of the President | 0.707 | yes |
87 | 2001 | 878 | Wyatt Earp | 0.704 | no |
88 | 2019 | 285984 | Last Bastion | 0.703 | no |
89 | 2019 | 283863 | The Magnificent | 0.702 | no |
90 | 2018 | 214887 | CO₂: Second Chance | 0.693 | yes |
91 | 2018 | 245638 | Coimbra | 0.693 | yes |
92 | 2018 | 245928 | Pax Emancipation | 0.688 | no |
93 | 2016 | 193739 | Jórvík | 0.684 | no |
94 | 2010 | 66362 | Glen More | 0.683 | yes |
95 | 2008 | 37380 | Roll Through the Ages: The Bronze Age | 0.680 | no |
96 | 2012 | 119890 | Agricola: All Creatures Big and Small | 0.676 | no |
97 | 2015 | 181687 | The Pursuit of Happiness | 0.670 | no |
98 | 2005 | 11825 | Empire of the Sun | 0.668 | no |
99 | 2014 | 154825 | Arkwright | 0.666 | yes |
100 | 2018 | 244049 | Forum Trajanum | 0.664 | no |
This section contains a variety of visualizations and metrics for assessing the performance of the model(s) during resampling. If you’re not particularly interested in predictive modeling, skip down further to the predictions from the model.
An easy way to examine the performance of classification model is to view a separation plot. We plot the predicted probabilities from the model for every game (from resampling) from lowest to highest. We then overlay a blue line for any game that the user does own. A good classifier is one that is able to separate the blue (games owned by the user) from the white (games not owned by the user), with most of the blue occurring at the highest probabilities (right side of the chart).
We can more formally assess how well each model did in resampling by looking at the area under the receiver operating characteristic curve. A perfect model would receive a score of 1, while a model that cannot predict the outcome will default to a score of 0.5. The extent to which something is a good score depends on the setting, but generally anything in the .8 to .9 range is very good while the .7 to .8 range is perfectly acceptable.
wflow_id | .metric | .estimator | .estimate |
GLM | roc_auc | binary | 0.91 |
Decision Tree | roc_auc | binary | 0.80 |
Another way to think about the model performance is to view its lift, or its ability to detect the positive outcomes over that of a null model. High lift indicates the model can much more quickly find all of the positive outcomes (in this case, games owned or played by the user), while a model with no lift is no better than random guessing. A gains chart is another way to view this.
While we are probably more interested in the lift provided by the models to evaluate their efficacy, we can also explore the optimal cutpoint if we wanted to define a hard threshold for identifying games a user will own vs not own.
The threshold we select depends on how we much we care about false positives (games the model predicts that the user does not own) vs false negatives (games the user owns that the model does not predict). We can toggle threshold to
Finally, we can understand the performance of the model by examining its calibration. If the model assigns a probability of 5%, how often does the outcome actually occur? A well calibrated model is one in which the predicted probabilities reflect the probabilities we would observe in the actual data. We can assess the calibration of a model by grouping its predictions into bins and assessing how often we observe the outcome versus how often our model expects to observe the outcome.
A model that is well calibrated will closely follow the dashed line - its expected probabilities match that of the observed probabilities. A model that consistently underestimates the probability of the event will be over this dashed line, be a while a model that overestimates the probability will be under the dashed line.
What games does the model think nakedmeeple is most likely to own that are not in their collection?
Published | ID | Name | Pr(Owned) | Owned |
2015 | 175878 | 504 | 0.989 | no |
2017 | 220308 | Gaia Project | 0.977 | no |
2019 | 270971 | Era: Medieval Age | 0.974 | no |
2016 | 200680 | Agricola (Revised Edition) | 0.949 | no |
2014 | 164928 | Orléans | 0.945 | no |
What games does the model think nakedmeeple is least likely to own that are in their collection?
Published | ID | Name | Pr(Owned) | Owned |
1960 | 148203 | Dutch Blitz | 0.002 | yes |
2009 | 46213 | Telestrations | 0.002 | yes |
1992 | 327 | Loopin' Louie | 0.002 | yes |
2012 | 124708 | Mice and Mystics | 0.003 | yes |
2015 | 180974 | Potion Explosion | 0.003 | yes |
Top 25 games most likely to be owned by the user in each year, highlighting in blue the games that the user has owned.
rank | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 |
1 | Terra Mystica | Glass Road | Fields of Arle | 504 | Agricola (Revised Edition) | Gaia Project | Everdell | Era: Medieval Age |
2 | The Great Zimbabwe | Patchistory | Orléans | Food Chain Magnate | Falling Sky: The Gallic Revolt Against Caesar | Pendragon: The Fall of Roman Britain | Book of Dragons | Watergate |
3 | Keyflower | Amerigo | AquaSphere | 7 Wonders Duel | Terraforming Mars | The Castles of Burgundy: The Dice Game | Newton | Tapestry |
4 | Pax Porfiriana | Concordia | La Granja | The Pursuit of Happiness | A Feast for Odin | Breaking Bad: The Board Game | Underwater Cities | Maracaibo |
5 | Andean Abyss | Legacy: The Testament of Duke de Crecy | Three Kingdoms Redux | Mombasa | The Castles of Burgundy: The Card Game | Pericles: The Peloponnesian Wars | Concordia Venus | Ragusa |
6 | Il Vecchio | Impulse | Splendor | Haspelknecht: The Story of Early Coal Mining | Hit Z Road | Calimala | Coimbra | Ancient Civilizations of the Inner Sea |
7 | Agricola: All Creatures Big and Small | Cuba Libre | Fire in the Lake | The Gallerist | Comanchería: The Rise and Fall of the Comanche Empire | The Ruhr: A Story of Coal Trade | CO₂: Second Chance | Pax Pamir: Second Edition |
8 | Space Cadets | Navajo Wars | Arkwright | Grand Austria Hotel | Scythe | Clans of Caledonia | Pax Emancipation | Sierra West |
9 | Robinson Crusoe: Adventures on the Cursed Island | Room 25 | Irish Gauge | The Bloody Inn | Liberty or Death: The American Insurrection | Lisboa | Forum Trajanum | Pipeline |
10 | Love Letter | Caverna: The Cave Farmers | Istanbul | The Voyages of Marco Polo | Jórvík | Twilight Imperium: Fourth Edition | Pandemic: Fall of Rome | Last Bastion |
11 | Tokaido | Bruges | Praetor | Churchill | Black Orchestra | Altiplano | TOKYO METRO | The Magnificent |
12 | Suburbia | Wildcatters | Camel Up | SteamRollers | Coal Baron: The Great Card Game | Here I Stand: 500th Anniversary Edition | Carpe Diem | Barrage |
13 | Kingdom of Solomon | Rococo | Imperial Settlers | DRCongo | The Oracle of Delphi | Merlin | Arkham Horror (Third Edition) | Hellenica: Story of Greece |
14 | Wallenstein (Second Edition) | Lewis & Clark: The Expedition | Belle of the Ball | Triumph & Tragedy: European Balance of Power 1936-1945 | Citadels | Notre Dame: 10th Anniversary | Hitler's Reich: WW2 in Europe | Crystal Palace |
15 | Virgin Queen | The Hunters: German U-Boats at War, 1939-43 | Roll Through the Ages: The Iron Age | Taluva Deluxe | Flamme Rouge | Time of Crisis: The Roman Empire in Turmoil, 235-284 AD | The Estates | Tiny Towns |
16 | CO₂ | Bruxelles 1893 | Nations: The Dice Game | Through the Ages: A New Story of Civilization | Great Western Trail | 878 Vikings: Invasions of England | Rising Sun | Amul |
17 | Archipelago | 1862: Railway Mania in the Eastern Counties | 1944: Race to the Rhine | Copper Country | Pax Renaissance | Wendake | Crown of Emara | Bios: Origins (Second Edition) |
18 | Clash of Cultures | Rialto | Roll for the Galaxy | Porta Nigra | Arkham Horror: The Card Game | Dinosaur Island | Cosmic Encounter: 42nd Anniversary Edition | Gandhi: The Decolonization of British India, 1917 – 1947 |
19 | 1989: Dawn of Freedom | The Little Prince: Make Me a Planet | Greenland | Trickerion: Legends of Illusion | Forged in Steel | Coal Country | New Frontiers | Marco Polo II: In the Service of the Khan |
20 | Divided Republic | 1775: Rebellion | Panamax | My Village | Pandemic: Iberia | Heaven & Ale | Tierra y Libertad: The Mexican Revolution Game (Second Edition) | Caylus 1303 |
21 | Ginkgopolis | Terror in Meeple City | Patchwork | Shogun Big Box | Codenames: Pictures | Summit: The Board Game | AuZtralia | Coloma |
22 | Le Havre: The Inland Port | Rebel Raiders on the High Seas | Akrotiri | Neanderthal | Silent Victory: U.S. Submarines in the Pacific, 1941-45 | Codenames: Disney – Family Edition | Brass: Birmingham | Fields of Fire 2 |
23 | Android: Netrunner | A Distant Plain | Pandemic: The Cure | The King Is Dead | Vinhos Deluxe Edition | Lignum (Second Edition) | Donning the Purple | The Isle of Cats |
24 | Merchant of Venus (Second Edition) | Bora Bora | Subdivision | Pax Pamir | Turin Market | Spirit Island | Blackout: Hong Kong | Aftermath |
25 | Kairo | Francis Drake | Castles of Mad King Ludwig | Lignum | Cottage Garden | Sagrada | Cataclysm: A Second World War | The Quest for El Dorado: The Golden Temples |
This is an interactive table for the model’s predictions for the training set (from resampling).
We’ll validate the model by looking at its predictions for games published in 2020. That is, how well did a model trained on a user’s collection through 2020 perform in predicting games for the user in 2020?
username | outcome | dataset | method | .metric | .estimate |
nakedmeeple | owned | validation | GLM | roc_auc | 0.923 |
nakedmeeple | owned | validation | Decision Tree | roc_auc | 0.883 |
Table of top 50 games from 2020, highlighting games that the user owns.
Published | ID | Name | Pr(Owned) | Owned |
2020 | 184267 | On Mars | 0.959 | yes |
2020 | 300322 | Hallertau | 0.934 | yes |
2020 | 253506 | Versailles 1919 | 0.928 | yes |
2020 | 272739 | Clinic: Deluxe Edition | 0.902 | yes |
2020 | 297486 | Ride the Rails | 0.700 | yes |
2020 | 281655 | High Frontier 4 All | 0.685 | no |
2020 | 300877 | New York Zoo | 0.680 | no |
2020 | 316377 | 7 Wonders (Second Edition) | 0.587 | no |
2020 | 292333 | Cowboys II: Cowboys & Indians Edition | 0.585 | no |
2020 | 318983 | Faiyum | 0.574 | no |
2020 | 304420 | Bonfire | 0.572 | no |
2020 | 206480 | Imperial Struggle | 0.550 | yes |
2020 | 242520 | All Bridges Burning: Red Revolt and White Guard in Finland, 1917-1918 | 0.515 | yes |
2020 | 189664 | The Hunted: Twilight of the U-Boats, 1943-45 | 0.514 | no |
2020 | 245224 | La Belle Époque | 0.501 | no |
2020 | 300001 | Renature | 0.479 | no |
2020 | 314040 | Pandemic Legacy: Season 0 | 0.479 | no |
2020 | 310442 | Feierabend | 0.452 | no |
2020 | 265784 | Cleopatra and the Society of Architects: Deluxe Edition | 0.442 | no |
2020 | 300327 | The Castles of Tuscany | 0.435 | no |
2020 | 299179 | Chancellorsville 1863 | 0.434 | no |
2020 | 276386 | Caesar: Rome vs. Gaul | 0.425 | yes |
2020 | 255456 | Beneath the Med: Regia Marina at Sea 1940-1943 | 0.392 | no |
2020 | 312251 | Curious Cargo | 0.371 | yes |
2020 | 246900 | Eclipse: Second Dawn for the Galaxy | 0.361 | no |
2020 | 256317 | Guild Master | 0.345 | no |
2020 | 294788 | Conqueror: Final Conquest | 0.345 | no |
2020 | 306481 | Tawantinsuyu: The Inca Empire | 0.333 | no |
2020 | 284742 | Honey Buzz | 0.331 | no |
2020 | 320819 | Dinner in Paris | 0.329 | no |
2020 | 308765 | Praga Caput Regni | 0.319 | yes |
2020 | 301919 | Pandemic: Hot Zone – North America | 0.317 | no |
2020 | 286021 | Free Market: NYC | 0.317 | no |
2020 | 316554 | Dune: Imperium | 0.314 | no |
2020 | 284378 | Kanban EV | 0.307 | yes |
2020 | 319966 | The King Is Dead: Second Edition | 0.291 | yes |
2020 | 306040 | Merv: The Heart of the Silk Road | 0.289 | no |
2020 | 233262 | Tidal Blades: Heroes of the Reef | 0.286 | no |
2020 | 315631 | Santorini: New York | 0.262 | no |
2020 | 298572 | Cosmic Encounter Duel | 0.246 | no |
2020 | 284217 | Rush M.D. | 0.237 | no |
2020 | 269810 | Nevada City | 0.234 | no |
2020 | 267009 | Rome & Roll | 0.233 | no |
2020 | 296151 | Viscounts of the West Kingdom | 0.204 | no |
2020 | 317985 | Beyond the Sun | 0.203 | yes |
2020 | 282954 | Paris | 0.199 | no |
2020 | 301716 | Glasgow | 0.198 | no |
2020 | 282922 | Windward | 0.198 | no |
2020 | 318084 | Furnace | 0.198 | yes |
2020 | 291457 | Gloomhaven: Jaws of the Lion | 0.197 | no |
We can then refit our model to the training and validation set in order to predict all upcoming games for the user.
Examine the top 100 upcoming games, highlighting in blue ones the user already owns.
Published | ID | Name | Pr(Owned) | Owned |
2021 | 343905 | Boonlake | 0.988 | no |
2021 | 342942 | Ark Nova | 0.853 | no |
2022 | 310873 | Carnegie | 0.849 | no |
2021 | 344277 | Corrosion | 0.843 | no |
2022 | 349793 | Age of Rome | 0.758 | no |
2022 | 314580 | Hamburg | 0.688 | no |
2021 | 338760 | Imperial Steam | 0.661 | no |
2021 | 262941 | Dominant Species: Marine | 0.658 | yes |
2021 | 339484 | Savannah Park | 0.640 | no |
2021 | 325022 | Coffee Traders | 0.618 | no |
2021 | 325698 | Juicy Fruits | 0.602 | no |
2021 | 281248 | Cape May | 0.566 | no |
2023 | 312959 | Rallyman: DIRT | 0.566 | no |
2021 | 338980 | Eastern Empires | 0.505 | no |
2021 | 292899 | Tribune | 0.471 | no |
2021 | 206509 | Bayonets & Tomahawks | 0.422 | yes |
2021 | 277700 | Merchants Cove | 0.408 | no |
2021 | 291859 | Riftforce | 0.407 | no |
2022 | 326945 | Castles of Mad King Ludwig: Collector's Edition | 0.397 | no |
2021 | 296577 | Red Flag Over Paris | 0.387 | yes |
2021 | 283387 | Rocketmen | 0.360 | no |
2022 | 304051 | Creature Comforts | 0.347 | no |
2021 | 249277 | Brazil: Imperial | 0.346 | no |
2021 | 298102 | Roll Camera!: The Filmmaking Board Game | 0.343 | no |
2021 | 326804 | Rorschach | 0.337 | no |
2021 | 308119 | Pax Renaissance: 2nd Edition | 0.335 | no |
2021 | 300323 | Conquest & Consequence | 0.312 | no |
2022 | 322499 | Red Dust Rebellion | 0.312 | no |
2021 | 251747 | Atlantic Chase | 0.304 | yes |
2022 | 300192 | Twilight Struggle: Red Sea – Conflict in the Horn of Africa | 0.299 | no |
2022 | 284189 | Foundations of Rome | 0.294 | no |
2021 | 196676 | Rome, Inc.: From Augustus to Diocletian | 0.290 | no |
2021 | 341169 | Great Western Trail (Second Edition) | 0.287 | no |
2021 | 304985 | Dark Ages: Holy Roman Empire | 0.284 | no |
2021 | 295535 | Dark Ages: Heritage of Charlemagne | 0.284 | no |
2022 | 291155 | Enemy Action: Kharkov | 0.284 | no |
2021 | 308989 | Bristol 1350 | 0.272 | no |
2021 | 319792 | Fly-A-Way | 0.264 | no |
2022 | 295103 | Almoravid: Reconquista and Riposte in Spain, 1085-1086 | 0.264 | no |
2021 | 318709 | For Sale Autorama | 0.263 | no |
2021 | 303954 | Pax Viking | 0.263 | no |
2022 | 280726 | Legacies | 0.263 | no |
2022 | 309913 | Border Reivers: Anglo-Scottish Border Raids, 1513-1603 | 0.262 | no |
2022 | 176588 | A Glorious Chance: The Naval Struggle for Lake Ontario in the War of 1812 | 0.251 | no |
2021 | 238799 | Messina 1347 | 0.244 | no |
2022 | 350205 | Horseless Carriage | 0.232 | no |
2022 | 354254 | Voices In My Head | 0.224 | no |
2021 | 290236 | Canvas | 0.219 | no |
2022 | 283137 | Human Punishment: The Beginning | 0.214 | no |
2022 | 266018 | Trinidad | 0.214 | no |
2021 | 314088 | Agropolis | 0.213 | no |
2021 | 298378 | Maharaja | 0.209 | no |
2022 | 305096 | Endless Winter: Paleoamericans | 0.207 | no |
2021 | 323156 | Stroganov | 0.201 | no |
2021 | 344768 | Mobile Markets: A Smartphone Inc. Game | 0.201 | no |
2022 | 331106 | The Witcher: Old World | 0.199 | no |
2023 | 347909 | Rogue Angels: Legacy of the Burning Suns | 0.195 | no |
2022 | 326175 | The Smoky Valley | 0.194 | no |
2021 | 320960 | Roll In One | 0.193 | no |
2022 | 330950 | Age of Galaxy | 0.189 | no |
2021 | 339958 | Gutenberg | 0.184 | no |
2021 | 286439 | Import / Export: Definitive Edition | 0.178 | no |
2021 | 324242 | Sheepy Time | 0.175 | no |
2022 | 305462 | The Age of Atlantis | 0.175 | no |
2021 | 341048 | Free Ride | 0.174 | no |
2021 | 260524 | Beyond Humanity: Colonies | 0.173 | no |
2022 | 190572 | 1941: Race to Moscow | 0.172 | no |
2021 | 305761 | Whale Riders | 0.172 | no |
2021 | 292375 | The Great Wall | 0.172 | no |
2022 | 325096 | Skies Above Britain | 0.170 | no |
2022 | 233958 | Plains Indian Wars | 0.169 | no |
2021 | 252752 | Genotype: A Mendelian Genetics Game | 0.164 | no |
2021 | 329591 | Ultimate Railroads | 0.164 | no |
2022 | 347703 | First Rat | 0.163 | no |
2021 | 259962 | Stress Botics | 0.159 | no |
2021 | 322195 | Kokopelli | 0.154 | no |
2021 | 274861 | Venice | 0.151 | no |
2021 | 298069 | Cubitos | 0.151 | no |
2021 | 299684 | Khôra: Rise of an Empire | 0.151 | no |
2021 | 285967 | Ankh: Gods of Egypt | 0.151 | no |
2021 | 292126 | Excavation Earth | 0.148 | no |
2022 | 275215 | Namiji | 0.148 | no |
2021 | 259066 | Commands & Colors: Samurai Battles | 0.145 | yes |
2022 | 294880 | Chai: Tea for 2 | 0.144 | no |
2021 | 339906 | The Hunger | 0.144 | no |
2022 | 317511 | Tindaya | 0.144 | no |
2021 | 314393 | Wutaki | 0.141 | no |
2021 | 326848 | Illumination | 0.140 | yes |
2021 | 328871 | Terraforming Mars: Ares Expedition | 0.139 | no |
2022 | 318450 | Bios: Mesofauna | 0.139 | no |
2021 | 301366 | Caves of Rwenzori | 0.139 | no |
2022 | 324090 | Scarface 1920 | 0.137 | no |
2021 | 328569 | Mint Bid | 0.131 | no |
2021 | 329670 | Pandemic: Hot Zone – Europe | 0.131 | no |
2021 | 259394 | Storm Above the Reich | 0.131 | no |
2022 | 300217 | Merchants of the Dark Road | 0.130 | no |
2021 | 258242 | Magnate: The First City | 0.128 | no |
2022 | 322524 | Bardsung | 0.127 | no |
2022 | 319807 | Shogun no Katana | 0.126 | no |
2022 | 237179 | Weather Machine | 0.126 | no |